ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"
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Computer Science > Computation and Language
Title:ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"
Abstract:Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS). This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-500, a dataset generated with more complex tool use, lower cost, and almost 100% pass rate. Experiments show that ToolGrad models outperform those trained on expensive baseline datasets and proprietary LLMs. The ToolGrad source code, dataset, and models are available at this https URL.
| Comments: | ACL 2026 Findings. Source code: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2508.04086 [cs.CL] |
| (or arXiv:2508.04086v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2508.04086
arXiv-issued DOI via DataCite
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Submission history
From: Zhongyi Zhou [view email][v1] Wed, 6 Aug 2025 05:04:00 UTC (1,062 KB)
[v2] Thu, 30 Apr 2026 20:59:05 UTC (1,367 KB)
[v3] Wed, 17 Jun 2026 05:09:38 UTC (1,143 KB)
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